37 results about "Auto segmentation" patented technology

Systems and methods for automated segmentation of anatomical structures, such as the human heart. The systems and methods employ convolutional neural networks (CNNs) to autonomously segment various parts of an anatomical structure represented by image data, such as 3D MRI data. The convolutional neural network utilizes two paths, a contracting path which includes convolution / pooling layers, and an expanding path which includes upsampling / convolution layers. The loss function used to validate the CNN model may specifically account for missing data, which allows for use of a larger training set. The CNN model may utilize multi-dimensional kernels (e.g., 2D, 3D, 4D, 6D), and may include various channels which encode spatial data, time data, flow data, etc. The systems and methods of the present disclosure also utilize CNNs to provide automated detection and display of landmarks in images of anatomical structures.

The present invention relates to an automatic segmentation method for a breast MRI focus based on inter-frame correlation. The method comprises: reading an MRI image; preprocessing the image, and performing coarse segmentation on the preprocessed image to determine an initial contour of a focus; and by adopting an improved C- V level set model method, performing fine segmentation on a coarsely segmented image that is obtained in the prior step, further refining the tumor contour on the basis of a coarsely segmented contour, and optimizing an obtained finely segmented result by combining inter-frame correlation. The method provided by the present invention has higher accuracy and can better segment the focus.

In accordance with one aspect, a system for generating a three-dimensional MRI image is provided. In one embodiment, the system includes a composite-image generation module, an auto-segmentation module and a 3D rendering module. The composite-image generation module is configured to register a plurality of gray-scale slices and generate a plurality of composite-color slices where each of the plurality of composite-color slices is generated from a group of registered gray-scale slices. The auto-segmentation module is configured to receive the plurality of composite-color slices and identify features within each of the composite-color slices. The 3D rendering module is configured to convert the plurality of auto-segmented, composite-color slices into a three-dimensional image.

A manual windowing with auto-segmentation assistance method and system are disclosed that include defining manual tags to be assigned to one or more windows via a tag selection module, assigning the defined manual tags to the one or more windows via a manual tag assignment module and auto-segmenting the one or more windows within the display medium by producing auto-segmentation tags via the auto-segmentation module. Furthermore the method and system according to this invention provide for mapping tags to the auto-segmentation tags via a tag mapping module and merging the manual tags and the auto-segmentation tags via a merging module.

The invention discloses a liver CT automatic segmentation method based on deep shape learning, and the method comprises the steps: firstly building a liver segmentation data set, carrying out the preprocessing, and carrying out the coarse segmentation of a liver CT through the liver segmentation; secondly, establishing a liver shape set, learning a liver shape by using a variational auto-encoder, constructing a geometrical shape regularization module, and then adding the geometrical shape regularization module into liver segmentation to obtain a liver segmentation model constrained by geometrical shape consistency for automatic segmentation of liver CT. According to the method, the expressed shape features are creatively added into the existing deep segmentation network through the regularization module, and shape prior information is introduced in the training process of the convolutional neural network, so that the regularity and generalization ability of the segmentation model can be improved, and the segmentation result is enabled to better conform to the medical anatomy characteristics of the standard liver. The method has the advantages of being automatic, high in precision and capable of being migrated and expanded, and automatic and accurate segmentation of the abdominal large organs, such as the liver, can be achieved.

Content items may be segmented and labeled by topic to provide for the capture, analysis, indexing, retrieval and / or distribution of information within information rich media, such as audio or video, with greater functionality, accuracy and speed. The segments and other related information may be stored in a database and made accessible to users through, for example, a search service and / or an on-demand service. Automatic segmentation may include receiving a text representation, calculating relevance intervals based on the text representation, determining a nodal representation based on the relevance intervals, and determining segments of the content item based on the nodal representation.

The invention relates to a fully automatic lumbar vertebral image segmentation method based on pre-emphasis strategy, The method comprises the following steps: a data generation method based on humanlumbar vertebrae structure and magnetic resonance contrast characteristics automatically generates a large number of spine magnetic resonance images with rich structural diversity and texture diversity of the spine, and completes the training of the lumbar vertebrae image segmentation model; Using the trained segmentation model, the automatic segmentation of vertebral body and intervertebral discin spinal MRI data is realized. The invention can solve the problem of the limitation of the data of the traditional training model, and has high model generalization ability. There are many kinds oflumbar magnetic resonance images which are adaptable to different hospitals, different scanning machines and different scanning parameters.

The system for determining the three-dimensional functional liver segment based on the medical image comprises an image input module, an image classification module, a functional image resolution enhancement module, an anatomical image liver segment automatic segmentation module, a three-dimensional functional liver segment acquisition module and a result output module. The functional influence resolution enhancement module performs resolution enhancement processing on the functional image to obtain a high-resolution enhanced functional image, and the anatomical image liver segment automatic segmentation module performs liver segment automatic segmentation on the anatomical image to obtain a refined three-dimensional liver segment anatomical image. A functional image and an anatomical image are input, the input images are classified, resolution enhancement processing is performed on the functional image, three-dimensional refining processing and registration fusion are performed on the anatomical image to obtain a three-dimensional functional liver segment, and an image result is output and imported into a radiotherapy plan design system for radiotherapy dose calculation. According to the invention, three-dimensional functional liver segment judgment can be realized, the method is used for radiotherapy, the segmentation precision is improved, and the treatment cost of a patient is reduced.

A color image segmentation method is provided. The color image segmentation method includes the steps of: (a) calculating a predetermined value representing the degree of difference form the color of peripheral pixels by using pixel values of an input image; (b) obtaining a converted image by converting a calculated value into a value of a predetermined scale; and (c) segmenting the converted image. According to the color image segmentation method, a robust and an automatic segmentation is possible, and a segmentation speed is high even when segmenting an image containing much noise.

Disclosed herein are techniques for enhancing the accuracy of atlas-based auto-segmentation (ABAS) using an automated structure classifier that was trained using a machine learning algorithm. Also disclosed is a technique for training the automated structure classifier using atlas data applied to the machine learning algorithm.

A manual windowing with auto-segmentation assistance method and system are disclosed that include defining manual tags to be assigned to one or more windows via a tag selection module, assigning the defined manual tags to the one or more windows via a manual tag assignment module and auto-segmenting the one or more windows within the display medium by producing auto-segmentation tags via the auto-segmentation module. Furthermore the method and system according to this invention provide for mapping tags to the auto-segmentation tags via a tag mapping module and merging the manual tags and the auto-segmentation tags via a merging module.

The present invention includes a composition, method, method of making, and a kit for using an enteric contrast agent formulation comprising an enteric contrast medium comprising particles comprising atoms of an element with an atomic number from 70 to 77, and a pharmaceutically acceptable vehicle in which the particles are dispersed.

The present invention proposes an automatic segmentation method for a tooth three-dimensional mesh model, including automatic curvature threshold selection operation, feature region skeletonization operation, using distance field constraint method to close gaps in feature regions, and distributing the segmented disjoint sets to different Four characteristic functions of color. The algorithm includes: initial feature area extraction, morphological operations, removal of redundant branches in the initial segmentation, closing gaps in the feature skeleton, and over-segmentation processing. Using the segmentation algorithm of the present invention has a high degree of automation, and each tooth in the tooth model can be completely segmented only by inputting the number of teeth, which greatly reduces the work pressure of medical workers.

The invention relates to a method for synchronously segmenting multiple organs of a lung. The method comprises the following steps: a, performing interactive data annotation on the multiple organs of the lung; b, performing data preprocessing on the data annotation; c, constructing a data set for the data preprocessing result; d, enhancing and expanding the data set; e, carrying out DC-U-Net network model training on the data set to obtain a DC-U-Net training model; f, carrying out automatic segmentation on multiple organs of the lung based on the DC-U-Net training model. According to the invention, lung parenchyma, blood vessel, bronchus and pulmonary nodule areas can be automatically extracted based on the deep learning technology, and synchronous segmentation is realized.

Disclosed is a computer-implemented method which encompasses registering a tracked imaging device such as a microscope having a known viewing direction and an atlas to a patient space so that a transformation can be established between the atlas space and the reference system for defining positions in images of an anatomical structure of the patient. Labels are associated with certain constituents of the images and are input into a learning algorithm such as a machine learning algorithm, for example a convolutional neural network, together with the medical images and an anatomical vector and for example also the atlas to train the learning algorithm for automatic segmentation of patient images generated with the tracked imaging device. The trained learning algorithm then allows for efficient segmentation and / or labelling of patient images without having to register the patient images to the atlas each time, thereby saving on computational effort.

Disclosed is a computer-implemented method which encompasses registering a tracked imaging device such as a microscope having a known viewing direction and an atlas to a patient space so that a transformation can be established between the atlas space and the reference system for defining positions in images of an anatomical structure of the patient. Labels are associated with certain constituents of the images and are input into a learning algorithm such as a machine learning algorithm, for example a convolutional neural network,together with the medical images and an anatomical vector and for example also the atlas to train the learning algorithm for automatic segmentation of patient images generated with the tracked imaging device. The trained learning algorithm then allows for efficient segmentation and / or labelling of patient images without having to register the patient images to the atlas each time, thereby saving on computational effort.

The invention discloses an unsupervised cervical cell image automatic segmentation method. The method comprises the following steps of 1) preprocessing a cervical cell image; 2) subjecting the preprocessed cell image to the pre-background rough segmentation, and extracting a region to which cells belong; 3) subjecting the roughly segmented cell image to the detection and segmentation of cell components, and segmenting cells of different types by using a rapid region convolution neural network; 4) detecting and segmenting the cell nucleuses of cervical cells; 5) according to the characteristic parameters of the cell nucleuses, screening the cell nucleuses to obtain final candidate cell nucleuses; 6) judging whether the cell types obtained in the step 3) are multicellular spheroids or not; if not, segmenting a cytoplasmic region by using an activity contour model and a prior template; otherwise, conducting the step 7); 7) conducting the post-treatment based on the segmentation result of cell nucleus and cytoplasm, and the domain knowledge. In this way, the effective segmentation of a whole cervical cell is completed.